Forecast Models of Urban Growth and Land Use
Change to Assess Human Vulnerability
Resulting from Threats to Water Resources
Cláudia Maria de Almeida1
07 January 2005
_____________________________________________________________________
1
Brazilian National Institute for Space Research (INPE), Division for Remote Sensing
(DSR), Avenida dos Astronautas, 1758 – SERE / Room 06, 12227-010, Jardim da Granja,
São José dos Campos, São Paulo, Brazil
{[email protected]}
1
Forecast Models of Urban Growth and Land Use
Change to Assess Human Vulnerability
Resulting from Threats to Water Resources
Abstract
Models for the simulation of urban land use change have continuously undergone refinements in the latest
decades, what rendered them powerful tools to support decision-making, policy making and initiatives in the
realm of town planning. They may meet the most diverse ends, such as the monitoring and control of urban
growth, evaluation of infrastructure investment needs, identification of conflicts between urban expansion and
site suitability, amongst others. This paper is committed to present a simulation model of urban land use
change for the town of Bauru, located in the central west of São Paulo State, Brazil. The thereof generated
forecasts indicate how the uncontrolled urban growth may represent serious risks to the water spring of the
town and provide thus subsidies for assessing the human dimensions of the vulnerability resulting from
threats to water resources due to processes of land use change taking place in the urban fringe of Bauru.
1. Introduction: Historical Perspective on Models of Urban
Growth and Land Use Change
1.1 Concepts and Definitions on Land Use Change and Modeling
Various are the definitions for the terms land, land use and land use change, and they vary
according to the purpose of application and the context of their use. Wolman (1987) cites
Stewart´s (1968) definition of land in the scope of natural sciences: “the term land is used
in a comprehensive, integrating sense … to refer to a wide array of natural resource
attributes in a profile from the atmosphere above the surface down to some meters below
the land surface. The main natural resource attributes are climate, land form, soil,
vegetation, fauna and water.”
In a more economical approach, Hoover and Giarratani (1984) state that land “first and
foremost denotes space … The qualities of land include, in addition, such attributes as the
topographic, structural, agricultural and mineral properties of the site; the climate; the
availability of clean air and water; and finally, a host of immediate environmental
characteristics such as quiet, privacy, aesthetic appearance, and so on.”
Land use, on its turn, denotes the human employment of land (Turner and Meyer, 1994).
FAO/IIASA (1993) states that “land use concerns the function or purpose for which the
land is used by the local human population and can be defined as the human activities
which are directly related to land, making use of its resources or having an impact on
them.”
According to Briassoulis (2000), land use change “… means quantitative changes in the
areal extent (increases or decreases) of a given type of land use…”. For Jones and Clark
(1997), it may involve either a) conversion of one type of use into another or b)
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modification of a certain type of land use, such as changes from high-income to lowincome residential areas (the buildings remaining physically and quantitatively unaltered).
As to the particular term “model”, it can be understood as the representation of a system,
which can be achieved through different languages: mathematical, logical, physical, iconic,
graphical, etc., and according to one or more theories (Novaes, 1981).
A system is a set of parts, presenting interdependence among its constituent components
and attributes (Chadwick, 1973). A theory, on its turn, can be defined as a set of connected
statements which, through logical constructs, supplies an explanation of a process,
behavior, or other phenomenon of interest as it exists in reality (Chapin and Kaiser, 1979;
Johnston et al. 1994).
In a general way, models can be basically classified according to the following typologies
(Echenique, 1968; Novaes, 1981):
- descriptive model: it aims at solely understanding the functioning of a system;
- explorative model: it is a descriptive model that involves the parametric analyses
of several states, by means of variations in the systems elements and their relations, with no
external interference upon it. These kind of models are meant for answering “what if”
questions;
- predictive model: it is an explorative model that involves the time variable,
comprising the projection of some basic elements;
- operational model: it renders available the interference of the modeler, who can
introduce exogenous factors in the system components and relations in a way to modify its
behavior1.
According to Batty, Popper´s concept of science as “a process of conjecture, then refutation
of problems, followed by tentative solutions, error-elimination and the redefinition of
problems (Batty, 1976 citing Popper, 1972)” is reflected in the development of urban
theories and models.
1.2 Evolutionary Overview on Urban Land Use Change Models
Theoretical and mathematical models have for long been created for purposes of urban
studies, aiming at clarifying processes of urban and regional change. One of the oldest
contributions in this sense is Von Thünen´s theory of concentric rings, dated back to 1826.
According to his rent-locational model, the most intensive use of land will be near the
center, and the rent or land values will decrease outwards (Perraton and Baxter, 1974).
Other similar approaches in economic theory is the classical triangle of industrial location
proposed by Weber in 1909, Christaller´s model of central places of 1933, and Lösch´s
theory of economic regions, developed in 1940 (Merlin, 1973; Perraton and Baxter, 1974).
1
A branch of operational models would be the prescriptive or normative models, which attempt to change the system
under analysis in some optimal way.
3
Following these simple economic-oriented achievements in urban modeling, a new
generation of computerized urban models came into play in the late 1950s and early 1960s,
(Alonso, 1960; Lowry, 1964) immediately following the advances in computational
facilities at that time and the advent of the Quantitative Revolution. Important drawbacks of
these early models concern their oversize, arbitrary and mechanistic structure and the fact
that they could only describe the urban structure at one cross-section in time, or at best,
compare these static structures incorporating some long-term and often imputed
equilibrium, what rendered them mere simulations of the static observable structure of
cities (Batty, 1976).
In an effort to overcome the shortcomings of the first generation urban models, a new
bunch of modeling accomplishments commits itself, amongst other things, to work on a
dynamic basis (Crecine, 1964; Hill, 1965; Paelinck, 1970), i.e. to present an explicit time
dimension, where inputs and outputs vary over time (Wegener et al., 1986). Even though
considerable refinements were introduced in this early generation of dynamic models in
terms of coping with the complex and sometimes recursive spatial interactions among
different activities in a city, of adding the (multi-level) time dimension in the quantitative
analyses and employing sophisticated mathematical and theoretical tools - e.g. differential
equations (Allen et al., 1981), these models remained fairly non-spatial, especially in the
sense that their results could not be spatially visualized.
In brief, the urban models developed from the 1950s until the mid 1980s, in a fairly general
sense, did not take the spatial dimension into account. When this happened, the urban space
was decoupled into units (usually zones defined according to trip generation, census
districts or other alike criteria), but the output of such models could not be spatially
handled. In fact, effective advances in the spatial representation of urban models occurred
only by the end of the 1980s, when cellular automata (CA) models started to be largely
applied, also impelled by the parallel development in computer graphics and in theoretical
branches of complexity, chaos, fractals and alike (Batty et al. 1997).
Stephen Wolfram, one of the most renowned theoreticians on cellular automata defines
them as: “… mathematical idealizations of physical systems in which space and time are
discrete, and physical quantities take on a finite set of discrete values. A cellular automaton
consists of a regular uniform lattice (or `array´), usually infinite in extent, with a discrete
variable at each site (`cell´). The variables at each site are updated simultaneously
(`synchronously´), based on the values of the variables in their neighborhood at the
preceding time step, and according to a definite set of `local rules´ (Wolfram, 1983, p.
603)”.
Cellular automata (CA) models have found applications in diverse fields, ranging from
statistical and theoretical physics to land use and land cover change, traffic engineering and
control, diseases spread, behavioral biology, amongst others.
The 1990s experienced successive improvements in urban CA models, which started to
incorporate environmental, socioeconomic and political dimensions (Phipps and Langlois,
1997), and were finally successful in articulating analysis factors of spatial micro and
macroscale (White et al., 1998). Leading theoretical progresses in the broader discipline of
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artificial intelligence (AI), such as expert systems, artificial neural networks and
evolutionary computation, have been lately included in the scope of CA simulations. As
stated in Almeida et al. (2003), methods recently embedded in CA models like those
employing contemporary pattern-fitting tools such as neural nets (Wu, 1998; Yeh and Li,
2001) and evolutionary learning (Papini et al. 1998) are revealing to be amongst the most
promising for the coming generation of urban CA modeling achievements.
2. A Case Study: Simulation and Forecasts of Urban Growth
and Land Use Change in the City of Bauru, SP, Brazil by Means
of a CA Model
2.1 Study Area
Bauru is considered the biggest crossing point among railways, waterway and highways in
Latin America. The city itself was born as a crossing point between railways during the
inward advance of the coffee culture in the XIX century. Still today, the city is mainly
shaped by the transport system: its urban framework is organized around four interregional
roads and the railway track, which still crosses the city core area.
Bauru is currently regarded as a dynamic regional development pole in the central-west
portion of São Paulo State, with an outstanding performance of tertiary activities
(commerce and services). In view of its strategic historic development conditions, the city
underwent a drastically fast urbanization process. These urbanization booms were followed
by speculative processes, leading to the formation of a discontinuous urban fabric, marked
by the scattered presence of empty areas, high-rise buildings in the central neighborhoods
(FIGURE 1), low occupation densities in the outer areas, and the existence of detached
predominantly low-income residential settlements orbiting around the city center.
FIGURE 1 – Bauru aerial view with high-rise buildings.
SOURCE: ITE-FCEB (1998).
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2.2 Simulation Model of Urban Land Use Change: 1967 - 2000
In order to carry out modeling experiments designed to generate forecasts of urban land use
change, it is necessary at first to get acquainted with the driving forces or variables
governing land use change throughout a sufficiently long time series. These variables
concern infra-structural and socio-economic aspects of the city under analysis. In the
particular case of this experiment, the simulation time interval ranged from 1967 to 2000,
comprising thirty-three years, in which the urban population increased about 500%, rising
from 61,592 to 310,442 inhabitants. The acquired knowledge with respect to the variables
role in delineating the spatial patterns of land use change must support the parameterization
of the forecast models, which represent the ultimate goal of modeling.
2.2.1 Exploratory Analysis and Variables Selection
The exploratory analysis consists in the initial stage of modeling, where a series of
procedures is executed aiming to evaluate the characteristics of the variables driving land
use change. This enables the choice of the minimum and at the same time the best set of
variables to explain a certain type of transition (e.g. from non-urban use to residential use).
The exploratory analysis as well as all the remaining stages of the modeling process are
accomplished for each consecutive simulation period. In the case of Bauru, these periods
were delimited according to the availability of official land use maps. As these maps were
released only in 1967, 1979, 1988, and 2000, the simulation time series contained three
periods: 1967-1979; 1979-1988 and 1988-2000.
Initially, the process of variables selection is based on a heuristic procedure, where maps
of variables are superimposed on the final land use map in vector format for the considered
simulation period (FIGURE 2), in a way to identify those more meaningful to explain the
different types of land use change.
FIGURE 2 – Illustrative image showing the visual analysis for the identification of variables determining the
transition from residential to mixed use in Bauru from 1979 to 1988. The buffers are distances to planned
roads; the red spots are areas of medium-high density; and the pink polygons correspond to social housing.
6
In order to avoid redundancy in the input data, i.e. select variables with a high degree of
spatial dependence or association, the variables underwent pair-wise statistical tests, like
the Cramer´s Index and the Joint Information Uncertainty, with the acceptable threshold set
to 0.5 (Bonham-Carter, 1994). When this value is surpassed, the variables can be combined
into one, or one of the two has to be discarded. In a general way, the variable that presents
the smallest spatial association with the considered land use transition, i.e. the one which
contributes less to explain the transition at issue is eliminated from the model.
2.2.2 Model Parameterization
Models for the simulation of urban land use change are commonly built upon basis of
empirical methods. In the case study here presented, the “weights of evidence” method has
been adopted, which is based on the Bayes´s theorem of conditional probability. This
theorem consists in ratios relating the areas of occurrence of certain variables to the areas
where the phenomena under study have taken place (land use transitions). In this way, for
each cell of the study area, the probability that a given type of land use change will happen
in face of the previous occurrence of certain variables is:
n
å
P x , y {R/S 1 ∩
%S 2 %
...S n }
∩…
O {R} . e
W
+
i=1
x ,y
,
=
n
t
1 +
O {R} .
åe
å
i =1
W
(1)
+
x ,y
j =1
where Px,y corresponds to the probability of a cell with coordinates x,y to undergo a land use
transition R (e.g. from non-urban to residential use); S1-n refer to the n variables (or
evidences) driving land use change; O {R} corresponds to the odds of R, which is the ratio
of the probability of occurring R (P{R}) to its complementary probability (P{R}); and W+
represents the positive weight of evidence, which is calculated through the statistical
method “weights of evidence” (Bonham-Carter, 1994), and indicates the attraction or
positive correlation between a certain evidence and a given land use transition.
2.2.3 Model Calibration
Likewise in the exploratory analysis, heuristic procedures were employed for the model
adjustment or calibration. Visual comparative analyses were conducted for each type of
land use change, considering preliminary simulation results, maps of land use transition and
of transition probabilities as well as the overlay of maps of variables upon the final land use
map in vector format. This comparison envisages identifying those variables or evidences
which are effectively concurring to explain the respective transitions from those which are
just noise in the modeling, and it has been employed for both the variables selection and the
definition of the internal parameters of the simulation software- DINAMICA2.
2
DINAMICA is a cellular automata model for the simulation of landscape dynamics (land use and land cover change),
developed by the Center for Remote Sensing of the Federal University of Minas Gerais (http://www.csr.ufmg.br). The
sofware was written in object-oriented C++ language, and its present version runs on 32 bit Windows © system (SoaresFilho et al., 2002).
7
2.2.4 Simulation and Results
Several simulations were carried out by the DINAMICA model, and the best results for
each simulation period are presented in FIGURE 3. Upon basis of the carried out
calibration process, it becomes evident that the probability of certain non-urban areas in the
city of Bauru to shelter residential settlements largely depends on the previous existence of
this type of settlements in their surroundings, because this implies the possibility of
extending existing nearby infrastructure. It also depends on the greater proximity of these
areas to commercial activities clusters and on the available accessibility to such areas.
As to the transition of non-urban areas to industrial use, there are three great driving forces:
the nearness of such areas to the previously existent industrial use and the availability of
road and railway access. This can be explained by the fact that in the industrial production
process, the output of certain industries represent the input of other ones, what raises the
need of rationalization and optimization of costs by the clustering of plants interrelated in
the same productive chain. Furthermore, plots in the vicinities of industrial areas are often
prone to be devaluated for other uses, what makes them rather competitive for the industrial
use.
Regarding the transition of non-urban areas to services use, three major factors are crucial:
the proximity of these areas to clusters of commercial activities, their closeness to areas of
residential use, and last but not least, their strategic location in relation to the main urban
roads of Bauru. The first factor accounts for the suppliers market (and in some cases also
consumers market) of services; the second factor represents the consumers market itself;
and the third and last factor corresponds to the accessibility for both markets related to the
services use.
The creation of leisure and recreation zones, on its turn, takes place in outer areas with
good accessibility, and sometimes along low and flat riverbanks, since these areas are
floodable and hence unsuitable for sheltering other urban uses.
And finally, the transition from residential use to mixed use3 supposes the availability of
water supply and the existence of good accessibility conditions. These areas also strive for
locating not too far away from central commercial areas, for they depend on the specialized
supply from these areas, but not too close to them either for competitiveness reasons.
It is worth stating that the natural characteristics of the physical environment have not been
considered as impedances to urban land use change and growth at a more generalized level,
since the city site is relatively flat, with mild slopes, and presents no outstanding condition
regarding soil, vegetation and conservation areas constraints.
3
The mixed use zone basically comprises the residential, commercial, and services use. These zones actually play the
role of urban sub-centers and represent a sort of secondary commercial centers enhancement, which at a later stage start to
attract services and social infrastructure equipments besides commercial activities themselves.
8
Land Use - 1979
Simulation - 1979
Land Use - 1988
Simulation - 1988
Land Use - 2000
Simulation - 2000
Non-urban use
Commercial use
Institutional use
Mixed use
Residential use
Industrial use
Services use
Recreational use
FIGURE 3 – Real land use and respective simulations for Bauru in the years 1979, 1988, and 2000.
9
2.2.5 Model Validation
In order to evaluate the model performance, a method based on multiple resolutions has
been employed (Constanza, 1989), where the `goodness-of-fit´ between the real and the
simulated image is assessed not on a pixel-per-pixel basis but rather by means of sampling
windows of different sizes, relaxing thus the rigidity of adjustment between reality and
simulations. For the first simulation period (1967-1979), the fit index ranged from 0.941 to
0.944; for the second period (1979-1988), from 0.896 to 0.903, and finally for the third
period (1988-2000), from 0.954 to 0.957.
2.3 Forecast Model of Urban Land Use Change: 2000 - 2007
With the knowledge on transition trends and patterns acquired by means of the simulation
experiments executed throughout sufficiently long time series, it is then possible to
generate forecasts scenarios of urban land use change. For purposes of simplification, this
study considers only three types of scenarios: a stationary one, which preserves the
transition trends observed in the most recent simulation period; an optimistic one, which
slightly overestimates these trends; and a pessimist scenario, which slightly underestimates
such trends.
In models designed to simulate land use change, there are two types of transition
probabilities: one global, which estimates the total amount of change for the study area,
regardless of the influence imposed by the respective variables; and a local one, calculated
for each cell taking into account such variables. In the generation of forecast scenarios, the
global probabilities are recalculated, whereas the values of the local transition probabilities
remain the same. In the case of stationary scenarios, the Markov chain is employed to
reckon the global transition probabilities, which consists in a mathematical model meant to
describe a certain type of process that moves in a sequence of steps and throughout a set of
states.
For the non-stationary scenarios, univariate linear regression models were adopted, where
the area of a certain type of land use in 2007 (dependent variable) is explained by means of
a demographic or economic indicator (independent variable), such as urban population or
local industrial GDP. As previously mentioned, upon basis of slight over and
underestimations of these indicators, optimistic and pessimist global transition probabilities
are respectively calculated. Different results of forecast scenarios for the year 2007 are
shown in FIGURE 4.
It is worth mentioning that the scenarios point to the maintenance of the ongoing trend of
peripheral residential settlements, which underutilize the infrastructure network and hence
raise the costs of infrastructure investments in face of their scattered occupation pattern
(SEPLAN, 1997). Another observed tendency indicated by the forecasts is the
strengthening of the urban expansion towards the southwest side of the town, exactly where
the spring for the city water supply is located. In this way, the current urban policies and
initiatives must immediately envisage efficient devices to inhibit such typologies of urban
expansion.
10
Land Use in 2000
Stationary Scenario – 2007
Optimistic Scenario - 2007
Pessimist Scenario – 2007
Non-urban use
Commercial use
Institutional use
Mixed use
Residential use
Industrial use
Services use
Recreational use
FIGURE 4 – Stationary, optimistic and pessimist scenarios for 2007 and real land use in 2000 - Bauru.
3. Human Dimensions of the Vulnerability Resulting from
Threats to Water Resources due to Processes of Urban Land Use
Change in the Urban Area of Bauru, SP, Brazil
3.1
Agents Driving Urban Land Use Change: Profile and Behavior
Changes caused in the urban scenario are impelled by diverse agents or social actors.
Enterpreneurs dedicated to services and commercial activities strive to optimize locational
advantages in the urban area of Bauru. They tend to settle close to main roads, not only
because of the need of an efficient accessibility to goods and clients, but also due to the fact
that these roads grant visibility to their businesses, attracting potential new clients.
11
Similarly, industrial plants also struggle for competitive sites, taking into account land
price, good acessibility conditions for the supply of raw material and output delivery as
well as the warranty of rationalization in the production process in view of the proximity to
other plants interrelated within the same productive chain.
Leisure and recreation zones, built and managed by local authorities, rely in terms of
locational choice on site amenities, like landscape assets and good accessibility conditions,
since the latter are crucial for the survival of such areas.
Peripheral residential areas are meant either for low-income or high-income dwellers. Highincome settlements tend to be detached from the main urban agglomeration for purposes of
larger plots, usually unavailable within the city fabric, and access to pleasant landscapes. In
the specific case of Bauru, there is only one high-income settlement isolated from the main
urban agglomeration, located to the east of the town. This type of settlements usually
observes legal environmental restrictions.
Peripheral low-income settlements usually correspond to spontaneous initiatives of poor
inhabitants or to informal undertakings carried out by ilegal land state enterpreneurs. These
settlements are commonly situated away from the main urban agglomeration due to the
reduced land prices. New peripheral settlements tend to be located close to previous ones,
because this implies the possibility of extending existing nearby infrastructure, such as
water supply, bus lines, public lighting, electric energy supply, etc. They also seek to be
near main acess roads, since this refers to the dwellers´ need of commuting to work places
and shops in central areas. Contrarily to high-income settlements, low standard settlements
commonly disregard environmental constraints, and not rarely take place in conservation
areas, like natural parks, forest reserves or water springs protection areas.
It becomes thus evident that the agents of land use change in the city of Bauru comply with
the logics of economic theories of urban growth and change, grounded on the key concept
of utility maximization (Papageorgiou and Pines, 2001; Zhou and Vertinsky, 2001), in
which there is a continuous search for optimal location, able to assure:
-
competitive real estate prices;
good accessibility conditions;
rationalization of transportation costs, and
a strategic location in relation to suppliers and consumers markets.
3.2 Impacts of Uncontrolled Urban Growth in the Vicinities of Water
Springs on Liveability and Human Health
Informal land occupation in areas of water springs protection impacts not only the
ecological balance of the natural environment but also brings about damages to the quality
of life of urban inhabitants who directly depend on such supply sources. The foremost
observed effects of informal settlements located in the surroundings of water springs are:
-
direct discharge of sewage into the water bodies;
12
-
destruction of riparian vegetation;
silting of water reservoirs;
floods;
disturbances in the hydrological balance of the region;
contamination of water resources;
and, consequently, shortage of water supply.
Considering that a reliable access to fresh water is a key component of `quality of life´, it is
sensible to state that uncontrolled urban expansion in the proximities of water sources
represents a serious threat to liveability, and ultimately, to human health not only of the
informal settlements dwellers, but also of the city´s inhabitants as a whole.
The term `quality of life´ is intertwined with the concept of `liveability´, which can be
briefly described as unique combinations of amenity values (open space, urban vegetation,
ecological services); historic and cultural heritage; location; and intangibles such as
character, landscape and `sense of place´ (Bell, 2000). Liveability very much concerns a
`here and now´ perspective and basically refers to “the things that people see when they
walk out the front door. While it is about the environment, it is not explicitly for the
environment” (Brook Lyndhurst Ltd., 2004).
Most of the consequences listed above directly impact liveability, like the occurrence of
floods, the unpleasant smell which might blow from the reservoirs, the undesirable visual
effect of depleted riparian vegetation and contaminated waters as well as breakages and
oscillations in the water supply. The spread of informal urban occupation in the vicinities
of water springs and the consequent discharge of waste water directly into them cause a
much more serious impact in human health, rendering both the communities living on the
springs margins (which collect untreated water from such reservoirs) and the inhabitants
dependent on this source supply vulnerable to infecto-contagious diseases, such as cholera,
diarrhea, hepatitis in its diverse forms, leptospirosis, and schistosomiasis, amongst others.
3.3 Some Considerations on Human Dimensions of the Vulnerability to
Environmental Changes
As stated by Hamza and Zetter (1998), “concentration of people and activities on safe sites
is not a source of vulnerability. But the unequal distribution of resources, the
marginalization of segments of the population and informal activities, and their exclusion
from planned and serviced areas is what forces people on unsafe sites; and then
vulnerability is a consequence”. This opinion finds support in Watts and Bohle´s argument,
which says that vulnerability is the “outcome between environmental and socio-economic
forces” (Watts and Bohle, 1993).
Vulnerability is also defined as “insecurity and sensitivity in the well-being of individuals,
households and communities in the face of a changing environment, and implicit in this
their responsiveness and resilience to risks that they face during such negative changes.
Environmental changes that threaten welfare can be ecological, economic, social and
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political, and they can take the form of sudden schocks, long-term trends or seasonal
cycles” (Moser, 1998).
As exposed by Blaikie and Brookfield (1987) and by Bayliss-Smith (1991), any definition
requires the identification of two dimensions of vulnerability: its sensitivity (magnitude of a
system´s response to an external event) and its resilience (the ease and rapidity of a
system´s recovery from stress. From the point of view of the human dimensions of
vulnerability, these two dimensions are closely interrelated to the economic, social,
institutional, educational and psychological skills of individuals, households and
communities to offer responsiveness and recovery capacity in face of stress or hardship.
In the particular case of the vulnerability resulting from threats to water resources in Bauru,
sensitivity and resilience should be jointly offered by the dwellers and local authorities. As
adaptive/mitigation measures to tackle the occurrence of breakages/fluctuations in the water
supply and the risk of infecto-contagious diseases, the following are worth mentioning:
-
-
spontaneous or induced relocation of squatters, with public assistance in both
cases;
implementation of sewerage systems in all legalized low-income settlements
located in the water spring protection areas;
creation of a permanent framework for water quality control and emergency
management, supported by the local civil defence, envisaging immediate access
to alternative supply sources, like aquifers and water import from neighboring
watersheds;
launching of environmental awareness campaigns concerning the importance of
the water resources conservation in the mass media and in public and private
schools.
And preventive public initiatives and actions to refrain the undesired urban sprawl in the
vicinities of the water spring would include severe legal restrictions to the approval of new
settlements; permanent inspection of spring protection areas; creation of anonymous
denouncement channels to alert the public authorities about the eventual mushrooming of
new informal settlements seeds; fiscal and institutional incentives for the implementation of
low-income settlements in the eastern and northern sectors of the city, since the spring is
located in the southwest side of the town; and finally, the construction of polarizing
undertakings in the eastern and northern portions of Bauru, such as malls, business centers,
thematic parks, in order to redirect urban expansion towards more environmentally suitable
areas.
In fact, forecast models of urban growth and land use change lie in the realm of auxiliary
tools to guide and support preventive measures meant to assure safe, comfortable and
environmentally sound conditions in the urban life scope. These models are thus designed
to anticipate risks and vulnerability circumstances, and accordingly, avoid or soften their
extent and/or gravity.
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4. Final Remarks and Directions for Future Work
According to Almeida et al. (2003), “cellular automata (CA) models have become popular
largely because they are tractable, generate a dynamics which can replicate traditional
processes of change through diffusion, but contain enough complexity to simulate
surprising and novel change as reflected in emergent phenomena. CA models are flexible in
that they provide a framework which is not overburdened with theoretical assumptions, and
which is applicable to space represented as a raster or grid. These models can thus be
directly connected to raster data surfaces used in proprietary geographic information
systems”.
Although urban dynamic models have been the target of severe criticism, mainly in view of
their reductionism and constraints to fully capture the inherent complexity of reality
(Briassoulis, 2000), it can be argued that they ought to exist, for they offer an incomparable
way of abstracting patterns, order, and main trends of real world dynamics.
Actually, these models should be conceived, handled, applied, and interpreted in a wise and
critical form, so that modelers, planners, public and private decision-makers as well as
citizens in a general way could take the best from what they can offer and sensibly
acknowledge their limits.
Such urban land use dynamics models demonstrate to be useful for the identification of
main urban growth vectors and their predominant land use trends, what enables local
planning authorities to manage and reorganize (if applicable) city growth according to the
environmental carrying capacity of concerned sites and to their present and foreseen
infrastructure availability. It is worth strengthening that the modeling forecasts presented in
this work focused on water springs, but forecast models of urban growth and land use
change can cope with any sort of threat to environmental resources produced by urban
expansion processes.
And as final words, Batty concisely exposes the key ideas lying behind the applications and
purposes of urban modelling when he says: “... There are many reasons for the
development of such models: their role to help scientists to understand urban phenomena
through analysis and experiment represents a traditional goal of science, yet urban
modelling is equally important in helping planners, politicians and the community to
predict, prescribe and invent the urban future (Batty, 1976, p. xx).”
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